Compressive sensing techniques can be used to reduce the number of measurements of a scene that need to be captured to acquire the information to represent an image. In compressive sensing, M compressive measurements representing a compressed version of an N-pixel image are acquired and stored in place of the raw data for each of the N pixels of the N-pixel image (M is less than N). For example, a “lens-less” camera includes an array of shutters that are selectively opened or closed to allow light from a scene to reach a detector in the lens-less camera. Each of the shutters corresponds to a pixel of the acquired image. Sets of the shutters are opened together to define apertures that allow light to fall on the detector so that the detector is able to perform measurements of the intensity of the light received from the scene. Each measurement is performed by the detector for a different set of open shutters that define a different aperture. The complete image can be reconstructed from a number of measurements that is significantly less than the number of pixels in the image, thereby compressing the information required to represent the image. Compressive sensing therefore eliminates or reduces the need for compressing the image after acquisition as is done in conventional systems where the raw-data for each of the N pixels representing the image is acquired first and then compressed (e.g., into a JPEG compressed image) using conventional compression techniques.